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The high cost of density functional theory has hitherto limited the ab initioprediction of equation of state (EOS). In this article, we employ a combinationof large scale computing, advanced simulation techniques, and smart datascience strategies to provide an unprecedented, ab initio performance analysisof the high explosive pentaerythritol tetranitrate (PETN). Comparison to bothexperiment and thermochemical predictions reveals important quantitativelimitations of DFT for EOS prediction, and thus the assessment of highexplosives. In particular, we find DFT predicts the energy of PETN detonationproducts to be systematically too high relative to the unreacted neatcrystalline material, resulting in an underprediction of the detonationvelocity, pressure, and temperature at the Chapman-Jouguet (CJ) state. Theenergetic bias can be partially accounted for by high-level electronicstructure calculations of the product molecules. We also demonstrate a modelingstrategy for mapping chemical composition across a wide parameter space withlimited numerical data, the results of which suggest additional molecularspecies to consider in thermochemical modeling.

First principles reactive simulation for equation of state prediction